Visualization of damage on images

ABSTRACT

A device may receive images of an object and information identifying the object, process the images using an artificial intelligence technique to identify parts of the object that are depicted in the images, and receive information identifying a location of damage on the object and information regarding the damage on the object. The device may process the information identifying the location of damage to identify a damaged part of the object, identify images depicting the damaged part, and identify, in the images, a location of the damaged part. The device may generate a first content item for display at the location of the damaged part in the images and generate a second content item for display with the images based on user interaction with the first content item, where the second content item includes information based on the information regarding the damage on the object.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/741,320, filed Jan. 13, 2020 (now U.S. Pat. No. 10,970,835), which isincorporated herein by reference in its entirety.

BACKGROUND

A vehicle dealership may obtain one or more images of a vehicle andprovide the one or more images and information regarding the vehicle toa web site for potential customers to view online.

SUMMARY

According to some implementations, a method may include receiving, by afirst device, a plurality of images of an object and informationidentifying the object; processing, by the first device, the pluralityof images using an artificial intelligence technique to identify one ormore parts of the object that are depicted in the plurality of images;receiving, by the first device, information identifying a location ofdamage on the object and information regarding the damage on the object;processing, by the first device, the information identifying thelocation of damage on the object to identify a damaged part of theobject; identifying, by the first device and from the plurality ofimages, one or more images depicting the damaged part; identifying, bythe first device and in the one or more images, a location of thedamaged part; generating, by the first device, a first content item fordisplay at the location of the damaged part in the one or more images;generating, by the first device, a second content item for display withthe one or more images, wherein the second content item includesinformation based on the information regarding the damage on the object;transmitting, by the first device and to a second device, the one ormore images of the object and the first content item for display at thelocation of the damaged part in the one or more images; andtransmitting, by the first device and to the second device, the secondcontent item for display with the one or more images based on userinteraction with the first content item.

According to some implementations, a device may include one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: receive a plurality of images of avehicle and information identifying the vehicle; process the pluralityof images using an artificial intelligence technique to identify one ormore parts of the vehicle that are depicted in the plurality of images,wherein the artificial intelligence technique includes at least one of amachine learning model or an image segmentation technique; receiveinformation identifying a location of damage on the vehicle andinformation regarding the damage on the vehicle; process the informationidentifying the location of damage on the vehicle to identify a damagedpart of the vehicle; identify, from the plurality of images, one or moreimages depicting the damaged part; identify, in the one or more images,a location of the damaged part; generate a first content item fordisplay at the location of the damaged part in the one or more images;generate a second content item for display with the one or more images,wherein the second content item includes information based on theinformation regarding the damage on the vehicle; transmit, to anotherdevice, the one or more images of the vehicle and the first content itemfor display at the location of the damaged part in the one or moreimages; and transmit, to the other device, the second content item fordisplay with the one or more images based on user interaction with thefirst content item.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a first device, may cause theone or more processors to: receive a plurality of images of an object;process the plurality of images using one or more artificialintelligence techniques to identify one or more parts of the object thatare depicted in the plurality of images; determine a location of damageon the object and information regarding the damage on the object;process the location of damage on the object to identify a damaged partof the object; identify, from the plurality of images, one or moreimages depicting the damaged part; identify, in the one or more images,a location of the damaged part; generate a first content item fordisplay at the location of the damaged part in the one or more images,wherein the first content item occupies less than a total number ofpixels depicting the damaged part; generate a second content item fordisplay with the one or more images, wherein the second content itemincludes a close-up image of damage on the damaged part; transmit theone or more images of the object and the first content item for displayat the location of the damaged part in the one or more images; andtransmit the second content item for display with the one or more imagesbased on user interaction with the first content item.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2 .

FIGS. 4-6 are flowcharts of an example process for visualization ofdamage on images.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A vehicle dealer (e.g., a car dealership, an individual selling avehicle, a boat dealership, and/or the like) may provide images of avehicle and information regarding the vehicle (e.g., a make, a model,and a year of the vehicle, a vehicle identification number (VIN), adescription of the condition of the vehicle (e.g., a description ofdamaged parts of the vehicle, a description of the condition of theinterior, and/or the like), and/or the like to a website for potentialcustomers to view online. Additionally, or alternatively, the vehicledealer may provide the images and information to an inventory managementsystem, which provides the images to the website. The website hostserver may process the images (e.g., to embed data in the images, toprovide enhanced user interfaces using the images, and/or the like). Toprovide images suitable for processing by the website (e.g., imageshaving uniform characteristics (e.g., resolutions, aspect ratios, colorconditions, lighting conditions, brightness conditions, orientationswith respect to the vehicle, and/or the like), images captured using thesame camera, and/or the like), the vehicle dealer may purchasespecialized equipment (e.g., a particular camera system, a standardbackdrop, a mount to achieve precise orientation of the camera with thevehicle, and/or the like), which consumes significant financialresources. If the vehicle dealer does not use specialized equipment,processing the images with non-uniform characteristics consumescomputing resources (e.g., processing resources, memory resources, powerresources, communication resources, and/or the like) and/or networkresources, and, in some cases, the website host server may not be ableto process the images adequately due to the non-uniform characteristics.Furthermore, when multiple vehicle dealers provide images to the websitehost server, the non-uniformity of the image characteristics increases,resulting in even greater consumption of computing resources (e.g.,processing resources, memory resources, power resources, communicationresources, and/or the like) and/or network resources to process theimages.

Some implementations described herein may provide an image annotationplatform that receives a plurality of images that may be captured with avariety of types of cameras, in a variety of lighting conditions, from avariety of orientations (e.g., without use of a particular camerasystem, a standard backdrop, a mount to achieve precise orientation ofthe camera with the vehicle, and/or the like) and/or may have aplurality of image characteristics, processes the images to identifyparts of the vehicle in the images, processes information (e.g.,provided by a vehicle dealer) identifying a location of damage on thevehicle, and generates content items for display at a location of adamaged part in one or more of the images. In some implementations, theimage annotation platform may process the plurality of images togenerate standardized images having common image characteristics and useone or more artificial intelligence techniques on the standardizedimages to identify parts of the vehicle in the images. In someimplementations, the image annotation platform may assign the pluralityof images to one or more classes of images based on the characteristicsof the plurality of images and use one or more artificial intelligencetechniques specific to the one or more classes to identify parts of thevehicle in the images. In this way, the image annotation platform mayreceive and process images having a plurality of image characteristicsand provide, based on information provided by vehicle dealers,visualizations of damage on the images of the vehicles, whileeliminating the need for vehicle dealers to purchase specializedequipment to sell vehicles on the website. Additionally, oralternatively, the image annotation platform may receive and processimages having a plurality of image characteristics, while conservingcomputing resources (e.g., processing resources, memory resources, powerresources, communication resources, and/or the like) and/or networkresources.

FIGS. 1A-1C are diagrams of one or more example implementations 100described herein. As shown in FIGS. 1A-1C, example implementation(s) 100includes a dealer device, an inventory management system, a website hostserver, an image annotation platform, and a user device. In thefollowing description, example implementation(s) 100 is described in acontext that relates to visualizing damage in images of vehicles.However, it will be appreciated that this description is forillustration purposes only, and that the techniques described herein maybe used to visualize information related to a condition of any suitableobject (e.g., a house or other real estate, furniture, jewelry,electronic equipment, musical instrument, and/or the like).

As shown in FIG. 1A, and by reference number 105, a dealer device (e.g.,a smartphone, a tablet, a digital camera, and/or the like) may captureimages of a vehicle. The dealer device may be associated with a vehicledealer (e.g., a car dealership, an individual selling a vehicle, a boatdealership, and/or the like). In some implementations, the dealer devicemay receive images of the vehicle from another device (e.g., from acamera and/or computer of a professional photographer, from a deviceowned by an employee of the vehicle dealer, and/or the like). In someimplementations, the dealer device (or the other device) may capturesome of the images of the vehicle at different times (e.g., on differentdays, after buying or receiving the vehicle and then again whenuploading information regarding the vehicle to an inventory managementsystem, and/or the like), different locations (e.g., one set of imagescaptured outside and another set of images captured in a showroom,and/or the like), and/or the like. As a result of the varying devices,times, and/or locations, the images of the vehicle may include imagesoriginating from a plurality of sources, having a plurality oforientations with respect to the vehicle, and/or having a plurality ofimage characteristics (e.g., a plurality of resolutions, a plurality ofaspect ratios, a plurality of color conditions, a plurality of lightingconditions (e.g., due to being captured at different locations and/ortimes), and/or the like).

Additionally, or alternatively, the dealer device may capture any numberof images of the vehicle and may capture different numbers of images fordifferent vehicles. For example, the dealer device may capture tenimages of a first vehicle and twenty images of a second vehicle.

In some implementations, the dealer device may capture images of thevehicle, where the images include one or more images of one or moredamaged parts of the vehicle. For example, the images may include aclose-up image of a scratch, crack, dent, discoloration, rust, and/orthe like on a part of the vehicle (e.g., a door, a hood, a roof, and/orthe like), a location of a missing part of the vehicle (e.g., a missingbutton, a missing gas cap, and/or the like), and/or the like.

In some implementations, the dealer device may receive (e.g., from thevehicle dealer performing a visual and/or mechanical inspection of thevehicle, from a database containing historical information regarding thevehicle, and/or the like) damage information regarding the vehicle. Thedamage information may include a description of one or more damagedparts of the vehicle, of one or more missing parts of the vehicle, ofconditions of one or more parts of the vehicle, and/or the like. Forexample, the damage information may include a description that thevehicle has a scratch on the hood.

In some implementations, the dealer device may receive vehicleinformation that identifies the vehicle (e.g., a make, a model, and ayear of the vehicle, a VIN, and/or the like). The dealer device mayreceive, from the vehicle dealer, input (e.g., via a touchscreen, akeyboard, an image of the VIN, an image of a license plate, and/or thelike) that includes the vehicle information. For example, the vehicledealer may provide to the dealer device and/or use the dealer device tocapture an image of the VIN, an image of the license plate, an image ofthe make or model of the vehicle, and/or the like, and use imageprocessing techniques (e.g., optical character recognition (OCR),computer vision, and/or the like) to identify the vehicle.

As shown in FIG. 1A, and by reference number 110, the dealer device maytransmit, to the inventory management system, the vehicle information,the damage information, and the images of the vehicle. In someimplementations, the dealer device may transmit, to the inventorymanagement system, the vehicle information and the images of the vehiclewithout obtaining or transmitting damage information. The inventorymanagement system may store the vehicle information, the damageinformation, and/or the images of the vehicle in a data structure thatincludes vehicle data associated with vehicles owned by the vehicledealer.

As shown in FIG. 1A, and by reference number 115, the inventorymanagement system may transmit dealer information (e.g., informationidentifying the vehicle dealer, and/or the like), the vehicleinformation, the damage information, and/or the images of the vehicle tothe website host server. In some implementations, the inventorymanagement system may transmit, at regular time intervals (e.g., hourly,daily, weekly, monthly, and/or the like), the dealer information, thevehicle information, the damage information, and/or the images of thevehicle and/or other vehicles for which the inventory management systemhas received new information and/or new images. For example, theinventory management system may transmit the information and/or imagesto the website host server to keep information and/or images on awebsite updated to reflect current inventory of the vehicle dealer.

Additionally, or alternatively, the inventory management system mayautomatically transmit, to the website host server, the dealerinformation, the vehicle information, the damage information, and/or theimages of the vehicle before, while, and/or after storing the vehicleinformation, the damage information, and/or the images of the vehicle inthe data structure. For example, the inventory management system mayreceive, from the dealer device, the vehicle information, the damageinformation, and/or the images of the vehicle, and concurrently storethe vehicle information, the damage information, and/or the images ofthe vehicle and transmit the dealer information, the vehicleinformation, the damage information, and/or the images of the vehicle tothe website host server.

In some implementations, the dealer device may transmit the dealerinformation, the vehicle information, the damage information, and/or theimages of the vehicle to the website host server. For example, ratherthan transmitting the vehicle information, the damage information,and/or the images of the vehicle to the inventory management system,which then transmits the information and/or images to the website hostserver, the dealer device may directly transmit the dealer information,the vehicle information, the damage information, and/or the images ofthe vehicle to the web site host server. Additionally, or alternatively,the dealer device may directly transmit the dealer information, thevehicle information, the damage information, and/or the images of thevehicle to the website host server and transmit the vehicle information,the damage information, and/or the images of the vehicle to theinventory management system.

As shown in FIG. 1B, and by reference number 120, the website hostserver may transmit the damage information and/or the images of thevehicle to the image annotation platform. In some implementations, thewebsite host server transmits the vehicle information, the damageinformation, and/or the images of the vehicle to the image annotationplatform automatically after receiving the vehicle information, thedamage information, and/or the images of the vehicle from the inventorymanagement system and/or the dealer device.

As shown in FIG. 1B, and by reference number 125, the image annotationplatform may receive and process the images of the vehicle and/or thedamage information to generate annotated images. In someimplementations, the image annotation platform may process the images ofthe vehicle to identify parts of the vehicle that are depicted in theimages of the vehicle. For example, the image annotation platform may beconfigured to use one or more artificial intelligence techniques, suchas machine learning, deep learning, and/or the like, to identify the oneor more parts of the vehicle that are depicted in the image data of thevehicle images. In some implementations, the one or more artificialintelligence techniques may include a computer vision technique, such asa convolutional neural network technique, to assist in classifying theimage data (e.g., data relating to distinctive feature points in theimage data) into a particular class (e.g., a class indicating that thefeature points share a planar surface and therefore belong to a commonpart). In some cases, the computer vision technique may identify the oneor more parts depicted in the image data using an image recognitiontechnique (e.g., an Inception framework, a ResNet framework, a VisualGeometry Group (VGG) framework, and/or the like), an object detectiontechnique (e.g. a Single Shot Detector (SSD) framework, a You Only LookOnce (YOLO) framework, and/or the like), an image segmentationtechnique, and/or the like.

Accordingly, using the one or more artificial intelligence techniques,the image annotation platform may identify one or more parts of thevehicle based on the image data corresponding to the vehicle in theimages of the vehicle. For example, as shown in FIG. 1B, the parts ofthe vehicle depicted in the image data may include a front bumper, frontheadlights, a hood, a windshield, a fender, passenger-side tires,passenger-side doors, a passenger-side mirror, a gas cap, a quarterpanel, and/or the like. Furthermore, using the one or more artificialintelligence techniques, the image annotation platform may identify anysuitable interior and/or exterior part of the vehicle.

For example, in one or more vehicle images, the hood of the vehicle maybe opened to reveal an engine bay, and the image annotation platform mayanalyze image data corresponding to the engine bay to identify an intakemanifold, a battery, an alternator, various fluid compartments, and/orthe like. In another example, one or more vehicle images may be obtainedfrom within the vehicle, and the image annotation platform may analyzeimage data from the one or more vehicle images to identify an instrumentpanel, an infotainment system, floor pedals, a steering wheel, a glovecompartment, and/or the like.

In some implementations, the image annotation platform may obtain one ormore virtual models of the vehicle (e.g., schematics based on a make,model, and/or year of the vehicle), which may be used to identify theone or more parts of the vehicle using the one or more artificialintelligence techniques. For example, the image annotation platform mayuse any number of artificial intelligence techniques, machine learningtechniques, deep learning techniques, and/or the like to generate one ormore virtual models that represent the one or more parts of the vehicle.The image annotation platform may train the one or more virtual modelsusing information that includes various parameters related to a visualappearance of parts associated with a specific vehicle (e.g., based onmake, model, and year), a visual appearance of parts associated withcertain vehicle types (e.g., sedans, convertibles, pickup trucks, sportutility vehicles, and/or the like), a visual appearance that certainparts typically have after a given level of wear-and-tear, a visualappearance of a vehicle as a whole, and/or the like, to output arepresentation of the one or more parts of the vehicle. In someimplementations, the one or more virtual models may be arrangedaccording to a hierarchy (e.g., a generic model could be used toclassify vehicle image data into a particular make, make and model,and/or the like, and additional models may be used to represent varioussubsystems, individual parts, and/or the like based on the particularmake, make and model, and/or the like).

In some implementations, as described with respect to FIG. 1A, thedealer device may provide images of the vehicle having a plurality ofimage characteristics (e.g., a plurality of resolutions, a plurality ofaspect ratios, a plurality of color conditions, a plurality ofbrightness conditions, a plurality of lighting conditions (e.g., due tobeing captured at different locations and/or times), and/or the like).In such circumstances, the image annotation platform may process theimages of the vehicle to have a common resolution, a common aspectratio, a common color condition, a common brightness condition, a commonlighting condition, and/or the like resulting in a set of standardizedimages of the vehicle. The image annotation platform may use the one ormore artificial intelligence techniques on the standardized images ofthe vehicle to identify the one or more parts of the vehicle. Using theone or more artificial intelligence techniques on the standardizedimages, rather than the images having a plurality of imagecharacteristics, conserves computing resources (e.g., processingresources, memory resources, power resources, communication resources,and/or the like) and/or network resources used to identify the one ormore parts of the vehicle.

Additionally, or alternatively, if the dealer device provides images ofthe vehicle having a plurality of image characteristics, the imageannotation platform may classify the images (e.g., each image, a subsetof the images, all of the images, and/or the like) of the vehicle basedon characteristics of the images. For each classification, the imageannotation platform may use one or more artificial intelligencetechniques specific to images having characteristics of theclassification to identify the one or more parts of the vehicle. Usingthe one or more artificial intelligence techniques specific to eachclassification of images conserves computing resources (e.g., processingresources, memory resources, power resources, communication resources,and/or the like) and/or network resources used to identify the one ormore parts of the vehicle.

In some implementations, the image annotation platform may process thedamage information to identify a location of damage on the vehicle andinformation regarding the damage on the vehicle. For example, the imageannotation platform may parse natural language descriptions in thedamage information to identify the location of damage on the vehicle(e.g., the name of one or more parts that are damaged, a location ofdamage on one or more parts, and/or the like) and information regardingthe damage (e.g., the damage is a scratch, crack, dent, discoloration,rust, and/or the like). In this way, the image annotation platform mayprocess the damage information to identify one or more damaged parts ofthe vehicle.

Additionally, or alternatively, the image annotation platform may useone or more artificial intelligence techniques, such as machinelearning, deep learning, and/or the like, to identify damage on thevehicle from the images of the vehicle. For example, one or more datamodels (e.g., machine learning models) may provide a representation ofan expected visual appearance of one or more parts of the vehicle (e.g.,when the one or more parts are new, after a certain level ofwear-and-tear, and/or the like) and the one or more artificialintelligence techniques may be used to identify, based on the one ormore images, one or more discrepancies between an actual visualappearance and the expected visual appearance of the one or more parts.Additionally, or alternatively, the one or more artificial intelligencetechniques may be used to identify, based on the one or more images, oneor more discrepancies between the actual visual appearance of differentparts, which may indicate relative conditions of the different parts(e.g., where a majority of the exterior body parts are a first shade ofa color and one particular exterior part, such as a hood, appears to bea second shade of the color, this may indicate that the one particularexterior part is newer than the various other exterior body parts, hasbeen repainted, and/or the like). In this way, the image annotationplatform may use one or more artificial intelligence techniques toidentify damage on the vehicle from the images of the vehicle and/or adamaged part of the vehicle.

In some implementations, the image annotation platform, afteridentifying the parts of the vehicle in the images of the vehicle andidentifying a damaged part of the vehicle, may identify one or moreimages depicting the damaged part. In other words, the image annotationplatform may identify which images of the vehicle provided by the webhosting server depict the damaged part (e.g., as identified by thedamage information). Furthermore, the image annotation platform mayidentify a location of the damaged part in the images depicting thedamaged part using, for example, the one or more artificial intelligencetechniques described above.

In some implementations, the image annotation platform may storeinformation (e.g., in a data structure) identifying the damaged part ofthe vehicle and the damage information in association with theinformation identifying the vehicle (e.g., the VIN). In suchcircumstances, if the image annotation platform receives another set ofimages of the vehicle (e.g., from another dealer device), the imageannotation platform may identify the location of the damaged part basedon the stored information. For example, the image annotation platformmay identify the parts of the vehicle in the other set of images usingthe one or more artificial intelligence techniques described above and,rather than processing damage information again, the image annotationplatform may access the stored information identifying the damaged partand locate the damaged part in the other set of images of the vehicle.In this way, the image annotation platform may conserve computingresources (e.g., processing resources, memory resources, powerresources, communication resources, and/or the like) and/or networkresources that would be consumed by processing damage information again.

In some implementations, the image annotation platform may generate afirst content item for display at the location of the damaged part inthe one or more images depicting the damaged part. For example, thefirst content item may include a damage annotation, a patterned overlay,a colored overlay, a hologram, a digital or virtual object, a visualmarker, a superimposed graphic, and/or the like. The first content itemmay include a geometric shape (e.g., a circle, a square, a triangle,and/or the like) for display at the location of the damaged part.

In some implementations, the image annotation platform may generatedifferent types of content items for different types of damage (e.g., ascratch, crack, dent, discoloration, rust, and/or the like). Forexample, the image annotation platform may generate a circle when thedamage is a scratch and a square when the damage is a dent.Additionally, or alternatively, the content items may vary by color,size, pattern, and/or the like based on the type of damage. In this way,the image annotation may generate similar content items for similartypes of damage and/or different content items for different types ofdamage.

In some implementations, the first content item may occupy less than atotal number of pixels depicting the damaged part. For example, thefirst content item may be smaller than the depicted damaged part (e.g.,a circle occupying a portion of a depicted surface area of a damagedhood, a triangle occupying less than a quarter of pixels depicting adamaged door, and/or the like). In this way, the first content item maynot obstruct the overall view of the vehicle but may still provide anindication that the part is damaged.

In some implementations, the image annotation platform may generate asecond content item for display with the one or more images depictingthe damaged part. The image annotation platform may generate the secondcontent item based on the damage information by, for example, generatinga text overlay, a text box, and/or the like that includes a descriptionof the damage. By way of example, the second content item may include atext box including the text “Small dent on lower panel of driver-sidedoor.”

In some implementations, the image annotation platform may generate thesecond content item based on images of the damaged part provided by thedealer device. For example, if the dealer device provides a close-upimage of a scratch, crack, dent, discoloration, rust, and/or the like,the second content item may include the close-up image. Additionally, oralternatively, the image annotation platform may generate a close-upimage of the damaged part based on one or more of the images provided bythe dealer by enlarging, zooming in on, and/or the like a portion of animage depicting the damaged part, and the second content item mayinclude the close-up image.

In some implementations, the image annotation platform may generate thesecond content item for display at a location within the one or moreimages depicting the damaged part. For example, the image annotationplatform may generate the second content item for display at a locationthat does not occupy pixels occupied by the damaged part and/or thevehicle within the one or more images depicting the damaged part (e.g.,the second content item does not overlap, cover, obstruct, and/or thelike the vehicle). In another example, the image annotation platform maygenerate the second content item for display at a location that occupiesless than a total number of pixels depicting the damaged part and/or thevehicle (e.g., less than half, a quarter, a tenth, and/or the like ofthe total number of pixels depicting the damaged part and/or thevehicle).

In some implementations, the image annotation platform may use the partsof the vehicle as anchor points to place content items (e.g., the firstcontent item, the second content item, and/or the like) in the images ofthe vehicle. The image annotation platform may establish a coordinatespace corresponding to a field of view of a device that captured theimages, and the content items may be generated within the coordinatespace according to a pose of the device. For example, the pose of thedevice may be expressed according to six degrees of freedom, whichinclude a three-dimensional position of the device combined with pitch,roll, and yaw representing an orientation through rotation about threeperpendicular axes. Accordingly, the one or more anchor points mayrepresent a relative position and orientation of one or morecorresponding points in the field of view of the device for an image ofthe vehicle, and a pose of the one or more anchor points within thecoordinate space may be automatically updated based on changes to thepose of the device in each of the images of the vehicle. When generatingthe content items, the content items may be placed within the coordinatespace using the one or more anchor points that correspond to the one ormore parts of the vehicle. When the pose of the device changes fromimage to image, the relative position and orientation of the one or moreanchor points may remain fixed to the one or more parts.

In this way, the image annotation platform may generate annotated imagesincluding content items (e.g., a first content item, a second contentitem, and/or the like) for display with one or more images depicting thedamaged part of the vehicle. For example, the image annotation platformmay generate annotated images including a damage annotation as a firstcontent item for display at the location of the damaged part in the oneor more images depicting the damaged part and a second content itemincluding information based on the damage information for display in theone or more images depicting the damaged part.

As shown in FIG. 1B, and by reference number 130, the image annotationplatform may transmit the annotated images to the website host server.In some implementations, the image annotation platform may transmit theannotated images to the website host server and instructions indicatingthat the second content item is to be displayed based on userinteraction with the first content item.

In some implementations, the website host server may be a server thathosts a website providing an online tool for users to search forvehicles offered for sale by a plurality of vehicle dealers, viewinformation regarding the vehicles, apply for a loan to purchase avehicle, and/or the like. As shown in FIG. 1B, and by reference number135, the user device may access and interact with the website hosted bythe website host server. In some implementations, the website hostserver may include an option on the website to permit a user to refineresults of a vehicle search by various characteristics of the vehicle(e.g., make, model, year, mileage, price, color, location of vehicledealer, and/or the like).

In some implementations, and as shown in FIG. 1B, the user device mayaccess and interact with the website host server to display images ofthe vehicle. For example, the website host server may transmit, to theuser device, the annotated images of the vehicle. As shown by referencenumber 140, the user device may display the annotated images of thevehicle including the first content item at the location of the damagedpart. For example, the first content item may include a damageannotation shown as a circle on a portion of the hood of the vehicle toindicate that the hood is damaged.

As shown in FIG. 1C, and by reference number 145, the user device mayreceive user interaction with the damage annotation (e.g., a touch on atouchscreen, a mouse hovering on or near the damage annotation, a mouseclick on or near the damage annotation, and/or the like). Based on theuser interaction, the user device may display the second content item.As shown in FIG. 1C, the second content item may include a close-upimage of a scratch on the hood of the vehicle displayed adjacent to thelocation of the damage part (e.g., at a location in the image that doesnot occupy pixels occupied by the damaged part and/or the vehicle,and/or the like).

In some implementations, the website host server may include a filteringoption permitting a user to view only images of damage parts of thevehicle. For example, the website host server may receive userinteraction selecting the filtering option and transmit the userinteraction to the image annotation platform. The image annotationplatform may receive the user selection and transmit, to the websitehost server, the one or more images depicting the damaged part withouttransmitting other images of the vehicle that do not depict the damagedpart for display by the user device. Additionally, or alternatively, theimage annotation platform, when transmitting the annotated images to thewebsite host server, as shown by reference number 130, may include adamage note in the one or more images depicting the damaged part (e.g.,in the metadata of the one or more images), and the website host server,based on the user interaction selecting the filtering option, maytransmit, to the user device for display, the images including thedamage note without transmitting other images of the vehicle.

In some implementations, the website host server may include a sortingoption permitting a user to view search results listing vehicles inorder based on the number of damage locations and/or defects on eachvehicle. For example, the image annotation platform may process theinformation identifying locations of damage on a vehicle to determine anumber of damage locations and/or defects on the vehicle, and repeat theprocessing for a plurality of vehicles to identify the number of damagelocations and/or defects on each vehicle. The website host server mayreceive user interaction selecting the sorting option and transmit theuser selection to the image annotation platform. The image annotationplatform may receive the user selection and transmit, to the websitehost server, search results listing the plurality of vehicles in orderbased on the number of damage locations and/or defects on each vehicle.Additionally, or alternatively, the image annotation platform, whentransmitting the annotated images to the website host server, as shownby reference number 130, may include a number of defects annotation forthe vehicle, and the website host server, based on the user interactionselecting the sorting option, may transmit, to the user device fordisplay, search results listing the plurality of vehicles in order basedon the number of defects on each vehicle.

In this way, the image annotation platform may receive and processimages having a plurality of image characteristics, provide, based oninformation provided by vehicle dealers, visualizations of damage on theimages of the vehicles for the website hosted by the website host serverand for display by the user device, while conserving computing resources(e.g., processing resources, memory resources, power resources,communication resources, and/or the like) and/or network resources.Additionally, or alternatively, the image annotation platform eliminatesthe need for vehicle dealers to purchase specialized equipment (e.g., aparticular camera system, a standard backdrop, a mount to achieveprecise orientation of the camera with the vehicle, and/or the like) tosell vehicles on the website.

As indicated above, FIGS. 1A-1C are provided as one or more examples.Other examples may differ from what is described with regard to FIGS.1A-1C. For example, there may be additional devices and/or networks,fewer devices and/or networks, different devices and/or networks, ordifferently arranged devices and/or networks than those shown in FIGS.1A-1C. Furthermore, two or more devices shown in FIGS. 1A-1C may beimplemented within a single device, or a single device shown in FIGS.1A-1C may be implemented as multiple and/or distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of example implementation(s) 100 may perform one or morefunctions described as being performed by another set of devices ofexample implementation(s) 100. For example, every operation describedherein as being performed by the image annotation platform may beperformed by the website host server, and vice versa.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG. 2, environment 200 may include a dealer device 210, an inventorymanagement system 220, a website host server 230, an image annotationplatform 240, a computing resource 245, a cloud computing environment250, a user device 260, and a network 270. Devices of environment 200may interconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

Dealer device 210 includes one or more devices capable of receiving,generating, storing, processing, displaying, and/or providinginformation associated with images of vehicles, damage on vehicles,identification of vehicles, identification of vehicle dealers, and/orthe like. For example, dealer device 210 may include a communicationand/or computing device, such as a mobile phone (e.g., a smart phone, aradiotelephone, and/or the like), a laptop computer, a tablet computer,a handheld computer, a desktop computer, a gaming device, a wearablecommunication device (e.g., a smart wristwatch, a pair of smarteyeglasses, and/or the like), or a similar type of device.

Inventory management system 220 includes one or more devices capable ofreceiving, generating, storing, processing, displaying, and/or providinginformation associated with images of vehicles, damage on vehicles,identification of vehicles, identification of vehicle dealers, vehicledealer inventory, and/or the like. For example, inventory managementsystem 220 may include one or more servers and/or computers to storeand/or provide information (e.g., numbers of vehicles, types ofvehicles, prices of vehicles, information about vehicles, images ofvehicles, and/or the like) associated with a vehicle dealer.

Website host server 230 includes a device capable of serving web content(e.g., web documents, HyperText Markup Language (HTML) documents, webresources, images, style sheets, scripts, text, and/or the like). Forexample, web site host server 230 may include a server and/or computingresources of a server, which may be included in a data center, a cloudcomputing environment, and/or the like. Website host server 230 mayprocess incoming network requests (e.g., from user device 260) usingHyperText Transfer Protocol (HTTP) and/or another protocol. Website hostserver 230 may store, process, and/or deliver web pages to user device260. In some implementations, communication between website host server230 and user device 260 may take place using HTTP.

Image annotation platform 240 includes one or more computing resourcesassigned to receiving, generating, storing, processing, and/or providinginformation associated with images of vehicles, damage on vehicles,identification of vehicles, identification of vehicle dealers, and/orthe like. For example, image annotation platform 240 may be a platformimplemented by cloud computing environment 250 that may receive,generate, store, process, and/or provide information related to imagesof vehicles, data models for representing an expected visual appearanceof one or more vehicle parts, content items for identifying damagedparts in images of a vehicles, and/or the like. In some implementations,image annotation platform 240 is implemented by computing resources 245of cloud computing environment 250.

Image annotation platform 240 may include a server device or a group ofserver devices. In some implementations, image annotation platform 240may be hosted in cloud computing environment 250. Notably, whileimplementations described herein describe image annotation platform 240as being hosted in cloud computing environment 250, in someimplementations, image annotation platform 240 may be non-cloud-based ormay be partially cloud-based. For example, image annotation platform 240may be a platform implemented by website host server 230.

Cloud computing environment 250 includes an environment that deliverscomputing as a service, whereby shared resources, services, and/or thelike may be provided to dealer device 210, inventory management system220, website host server 230, image annotation platform 240, and/or userdevice 260. Cloud computing environment 250 may provide computation,software, data access, storage, and/or other services that do notrequire end-user knowledge of a physical location and configuration of asystem and/or a device that delivers the services. As shown, cloudcomputing environment 250 may include image annotation platform 240and/or computing resource 245.

Computing resource 245 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource245 may host image annotation platform 240. The cloud resources mayinclude compute instances executing in computing resource 245, storagedevices provided in computing resource 245, data transfer devicesprovided by computing resource 245, and/or the like. In someimplementations, computing resource 245 may communicate with othercomputing resources 245 via wired connections, wireless connections, ora combination of wired and wireless connections.

As further shown in FIG. 2 , computing resource 245 may include a groupof cloud resources, such as one or more applications (“APPs”) 245-1, oneor more virtual machines (“VMs”) 245-2, virtualized storage (“VSs”)245-3, one or more hypervisors (“HYPs”) 245-4, or the like.

Application 245-1 includes one or more software applications that may beprovided to or accessed by dealer device 210, inventory managementsystem 220, website host server 230, image annotation platform 240,and/or user device 260. Application 245-1 may eliminate a need toinstall and execute the software applications on dealer device 210,inventory management system 220, website host server 230, imageannotation platform 240, and/or user device 260. For example,application 245-1 may include software associated with image annotationplatform 240 and/or any other software capable of being provided viacloud computing environment 250. In some implementations, oneapplication 245-1 may send/receive information to/from one or more otherapplications 245-1, via virtual machine 245-2.

Virtual machine 245-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 245-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 245-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 245-2 may execute on behalf of a user(e.g., dealer device 210, inventory management system 220, website hostserver 230, image annotation platform 240, and/or user device 260), andmay manage infrastructure of cloud computing environment 250, such asdata management, synchronization, or long-duration data transfers.

Virtualized storage 245-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 245. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 245-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 245.Hypervisor 245-4 may present a virtual operating platform to the “guestoperating systems” and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

User device 260 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith websites, images of vehicles, damage on vehicles, identification ofvehicles, identification of vehicle dealers, and/or the like. Forexample, user device 260 may include a communication and/or computingdevice, such as a mobile phone (e.g., a smart phone, a radiotelephone,etc.), a laptop computer, a tablet computer, a handheld computer, adesktop computer, a gaming device, a wearable communication device(e.g., a smart wristwatch, a pair of smart eyeglasses, etc.), or asimilar type of device.

Network 270 includes one or more wired and/or wireless networks. Forexample, network 270 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, and/or the like), a public land mobile network(PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2 . Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to dealer device 210, inventory management system 220,website host server 230, image annotation platform 240, computingresource 245, and/or user device 260. In some implementations, dealerdevice 210, inventory management system 220, website host server 230,image annotation platform 240, computing resource 245, and/or userdevice 260 may include one or more devices 300 and/or one or morecomponents of device 300. As shown in FIG. 3 , device 300 may include abus 310, a processor 320, a memory 330, a storage component 340, aninput component 350, an output component 360, and a communicationinterface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for visualizing damagein images. In some implementations, one or more process blocks of FIG. 4may be performed by a first device (e.g., image annotation platform240). In some implementations, one or more process blocks of FIG. 4 maybe performed by another device or a group of devices separate from orincluding the first device, such as a dealer device (e.g., dealer device210), an inventory management system (e.g., inventory management system220), a website host server (e.g., website host server 230), a computingresource (e.g., computing resource 245), a user device (e.g., userdevice 260), and/or the like.

As shown in FIG. 4 , process 400 may include receiving a plurality ofimages of an object and information identifying the object (block 405).For example, the first device (e.g., using computing resource 245,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370, and/or the like) mayreceive a plurality of images of an object and information identifyingthe object, as described above.

As further shown in FIG. 4 , process 400 may include processing theplurality of images using an artificial intelligence technique toidentify one or more parts of the object that are depicted in theplurality of images (block 410). For example, the first device (e.g.,using computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may process the plurality of imagesusing an artificial intelligence technique to identify one or more partsof the object that are depicted in the plurality of images, as describedabove.

As further shown in FIG. 4 , process 400 may include receivinginformation identifying a location of damage on the object andinformation regarding the damage on the object (block 415). For example,the first device (e.g., using computing resource 245, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may receiveinformation identifying a location of damage on the object andinformation regarding the damage on the object, as described above.

As further shown in FIG. 4 , process 400 may include processing theinformation identifying the location of damage on the object to identifya damaged part of the object (block 420). For example, the first device(e.g., using computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may process the information identifyingthe location of damage on the object to identify a damaged part of theobject, as described above.

As further shown in FIG. 4 , process 400 may include identifying, fromthe plurality of images, one or more images depicting the damaged part(block 425). For example, the first device (e.g., using computingresource 245, processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may identify, from the plurality of images, one or more imagesdepicting the damaged part, as described above.

As further shown in FIG. 4 , process 400 may include identifying, in theone or more images, a location of the damaged part (block 430). Forexample, the first device (e.g., using computing resource 245, processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370, and/or the like) mayidentify, in the one or more images, a location of the damaged part, asdescribed above.

As further shown in FIG. 4 , process 400 may include generating a firstcontent item for display at the location of the damaged part in the oneor more images (block 435). For example, the first device (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may generate a first content item for display atthe location of the damaged part in the one or more images, as describedabove.

As further shown in FIG. 4 , process 400 may include generating a secondcontent item for display with the one or more images wherein the secondcontent item includes information based on the information regarding thedamage on the object (block 440). For example, the first device (e.g.,using computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may generate a second content item fordisplay with the one or more images, as described above. In someimplementations, the second content item includes information based onthe information regarding the damage on the object.

As further shown in FIG. 4 , process 400 may include transmitting, to asecond device, the one or more images of the object and the firstcontent item for display at the location of the damaged part in the oneor more images (block 445). For example, the first device (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may transmit, to a second device, the one or moreimages of the object and the first content item for display at thelocation of the damaged part in the one or more images, as describedabove.

As further shown in FIG. 4 , process 400 may include transmitting, tothe second device, the second content item for display with the one ormore images based on user interaction with the first content item (block450). For example, the first device (e.g., using computing resource 245,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370, and/or the like) maytransmit, to the second device, the second content item for display withthe one or more images based on user interaction with the first contentitem, as described above.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, process 400 may include receiving, from thesecond device, user selection of a filtering option, and transmitting,to the second device, the one or more images depicting the damaged partwithout transmitting one or more other images, of the plurality ofimages, that do not depict the damaged part.

In a second implementation, alone or in combination with the firstimplementation, the second content item is transmitted for display in alocation within the one or more images adjacent to the location of thedamaged part in the one or more images.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, process 400 may include receivinginformation identifying a plurality of objects and informationidentifying locations of damage on the plurality of objects, processingthe information identifying locations of damage on the plurality ofobjects to determine a number of defects on each object of the pluralityof objects, receiving, from the second device, a user selection of asorting option, and transmitting, to the second device, search resultslisting the plurality of objects in order based on the number of defectson each object.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the plurality of images: have aplurality of resolutions, have a plurality of aspect ratios, have aplurality of color conditions, have a plurality of lighting conditions,have a plurality of brightness conditions, have a plurality oforientations with respect to the object, or originate from a pluralityof sources.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, processing the plurality ofimages using the artificial intelligence technique includes processingthe plurality of images to standardize the plurality of images to form astandardized plurality of images, wherein the standardized plurality ofimages has a common resolution, a common aspect ratio, a common colorcondition, and/or a common brightness condition, and processing thestandardized plurality of images to identify the one or more parts ofthe object that are depicted in the standardized plurality of images.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, processing the plurality ofimages using the artificial intelligence technique includes assigningthe plurality of images to one or more classes of images based oncharacteristics of the plurality of images and processing one or more ofthe plurality of images, assigned to one of the one or more classes ofimages, using an artificial intelligence technique specific to the oneof the one or more classes of images to identify one or more parts ofthe object that are depicted in the one or more of the plurality ofimages.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4 . Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for visualizing damagein images. In some implementations, one or more process blocks of FIG. 5may be performed by a device (e.g., image annotation platform 240). Insome implementations, one or more process blocks of FIG. 5 may beperformed by another device or a group of devices separate from orincluding the device, such as a dealer device (e.g., dealer device 210),an inventory management system (e.g., inventory management system 220),a website host server (e.g., website host server 230), a computingresource (e.g., computing resource 245), a user device (e.g., userdevice 260), and/or the like.

As shown in FIG. 5 , process 500 may include receiving a plurality ofimages of a vehicle and information identifying the vehicle (block 505).For example, the device (e.g., using computing resource 245, processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370, and/or the like) may receivea plurality of images of a vehicle and information identifying thevehicle, as described above.

As further shown in FIG. 5 , process 500 may include processing theplurality of images using an artificial intelligence technique toidentify one or more parts of the vehicle that are depicted in theplurality of images wherein the artificial intelligence techniqueincludes at least one of a machine learning model or an imagesegmentation technique (block 510). For example, the device (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may process the plurality of images using anartificial intelligence technique to identify one or more parts of thevehicle that are depicted in the plurality of images, as describedabove. In some implementations, the artificial intelligence techniqueincludes at least one of a machine learning model or an imagesegmentation technique.

As further shown in FIG. 5 , process 500 may include receivinginformation identifying a location of damage on the vehicle andinformation regarding the damage on the vehicle (block 515). Forexample, the device (e.g., using computing resource 245, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may receiveinformation identifying a location of damage on the vehicle andinformation regarding the damage on the vehicle, as described above.

As further shown in FIG. 5 , process 500 may include processing theinformation identifying the location of damage on the vehicle toidentify a damaged part of the vehicle (block 520). For example, thedevice (e.g., using computing resource 245, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may process theinformation identifying the location of damage on the vehicle toidentify a damaged part of the vehicle, as described above.

As further shown in FIG. 5 , process 500 may include identifying, fromthe plurality of images, one or more images depicting the damaged part(block 525). For example, the device (e.g., using computing resource245, processor 320, memory 330, storage component 340, input component350, output component 360, communication interface 370, and/or the like)may identify, from the plurality of images, one or more images depictingthe damaged part, as described above.

As further shown in FIG. 5 , process 500 may include identifying, in theone or more images, a location of the damaged part (block 530). Forexample, the device (e.g., using computing resource 245, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may identify, in theone or more images, a location of the damaged part, as described above.

As further shown in FIG. 5 , process 500 may include generating a firstcontent item for display at the location of the damaged part in the oneor more images (block 535). For example, the device (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may generate a first content item for display atthe location of the damaged part in the one or more images, as describedabove.

As further shown in FIG. 5 , process 500 may include generating a secondcontent item for display with the one or more images wherein the secondcontent item includes information based on the information regarding thedamage on the vehicle (block 540). For example, the device (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may generate a second content item for displaywith the one or more images, as described above. In someimplementations, the second content item includes information based onthe information regarding the damage on the vehicle.

As further shown in FIG. 5 , process 500 may include transmitting, toanother device, the one or more images of the vehicle and the firstcontent item for display at the location of the damaged part in the oneor more images (block 545). For example, the device (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may transmit, to another device, the one or moreimages of the vehicle and the first content item for display at thelocation of the damaged part in the one or more images, as describedabove.

As further shown in FIG. 5 , process 500 may include transmitting, tothe other device, the second content item for display with the one ormore images based on user interaction with the first content item (block550). For example, the device (e.g., using computing resource 245,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370, and/or the like) maytransmit, to the other device, the second content item for display withthe one or more images based on user interaction with the first contentitem, as described above.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the second content item includes a close-upimage of damage on the damaged part.

In a second implementation, alone or in combination with the firstimplementation, process 500 may include receiving, from the otherdevice, user selection of a filtering option and transmitting, to theother device, the one or more images depicting the damaged part withouttransmitting one or more other images, of the plurality of images, thatdo not depict the damaged part.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, process 500 may include storing,in a data structure, information identifying the damaged part of thevehicle and the information regarding the damage on the vehicle inassociation with the information identifying the vehicle, receivinganother plurality of images of the vehicle, and identifying, in theother plurality of images, a location of the damaged part in one or moreof the other plurality of images based on the information identifyingthe damaged part of the vehicle.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the plurality of images: have aplurality of resolutions, have a plurality of aspect ratios, have aplurality of color conditions, have a plurality of lighting conditions,have a plurality of brightness conditions, have a plurality oforientations with respect to the object, or originate from a pluralityof sources.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, process 500 may include, whenprocessing the plurality of images using the artificial intelligencetechnique, process the plurality of images to standardize the pluralityof images to form a standardized plurality of images, wherein thestandardized plurality of images has a common resolution, a commonaspect ratio, a common color condition, and/or a common brightnesscondition, and processing the standardized plurality of images toidentify the one or more parts of the vehicle that are depicted in thestandardized plurality of images.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, process 500 may include, whenprocessing the plurality of images using the artificial intelligencetechnique, assigning the plurality of images to one or more classes ofimages based on characteristics of the plurality of images andprocessing one or more of the plurality of images, assigned to one ofthe one or more classes of images, using an artificial intelligencetechnique specific to the one of the one or more classes of images toidentify one or more parts of the vehicle that are depicted in the oneor more of the plurality of images.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for visualizing damagein images. In some implementations, one or more process blocks of FIG. 6may be performed by an image annotation platform (e.g., image annotationplatform 240). In some implementations, one or more process blocks ofFIG. 6 may be performed by another device or a group of devices separatefrom or including the image annotation platform, such as a dealer device(e.g., dealer device 210), an inventory management system (e.g.,inventory management system 220), a website host server (e.g., websitehost server 230), a computing resource (e.g., computing resource 245), auser device (e.g., user device 260), and/or the like.

As shown in FIG. 6 , process 600 may include receiving a plurality ofimages of an object (block 605). For example, the image annotationplatform (e.g., using computing resource 245, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may receive a plurality ofimages of an object, as described above.

As further shown in FIG. 6 , process 600 may include processing theplurality of images using one or more artificial intelligence techniquesto identify one or more parts of the object that are depicted in theplurality of images (block 610). For example, the image annotationplatform (e.g., using computing resource 245, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may process the pluralityof images using one or more artificial intelligence techniques toidentify one or more parts of the object that are depicted in theplurality of images, as described above.

As further shown in FIG. 6 , process 600 may include determining alocation of damage on the object and information regarding the damage onthe object (block 615). For example, the image annotation platform(e.g., using computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may determine a location of damage onthe object and information regarding the damage on the object, asdescribed above.

As further shown in FIG. 6 , process 600 may include processing thelocation of damage on the object to identify a damaged part of theobject (block 620). For example, the image annotation platform (e.g.,using computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may process the location of damage onthe object to identify a damaged part of the object, as described above.

As further shown in FIG. 6 , process 600 may include identifying, fromthe plurality of images, one or more images depicting the damaged part(block 625). For example, the image annotation platform (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may identify, from the plurality of images, one ormore images depicting the damaged part, as described above.

As further shown in FIG. 6 , process 600 may include identifying, in theone or more images, a location of the damaged part (block 630). Forexample, the image annotation platform (e.g., using computing resource245, processor 320, memory 330, storage component 340, input component350, output component 360, communication interface 370, and/or the like)may identify, in the one or more images, a location of the damaged part,as described above.

As further shown in FIG. 6 , process 600 may include generating a firstcontent item for display at the location of the damaged part in the oneor more images wherein the first content item occupies less than a totalnumber of pixels depicting the damaged part (block 635). For example,the image annotation platform (e.g., using computing resource 245,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370, and/or the like) maygenerate a first content item for display at the location of the damagedpart in the one or more images, as described above. In someimplementations, the first content item occupies less than a totalnumber of pixels depicting the damaged part.

As further shown in FIG. 6 , process 600 may include generating a secondcontent item for display with the one or more images wherein the secondcontent item includes a close-up image of damage on the damaged part(block 640). For example, the image annotation platform (e.g., usingcomputing resource 245, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may generate a second content item for displaywith the one or more images, as described above. In someimplementations, the second content item includes a close-up image ofdamage on the damaged part.

As further shown in FIG. 6 , process 600 may include transmitting theone or more images of the object and the first content item for displayat the location of the damaged part in the one or more images (block645). For example, the image annotation platform (e.g., using computingresource 245, processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may transmit the one or more images of the object and thefirst content item for display at the location of the damaged part inthe one or more images, as described above.

As further shown in FIG. 6 , process 600 may include transmitting thesecond content item for display with the one or more images based onuser interaction with the first content item (block 650). For example,the image annotation platform (e.g., using computing resource 245,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370, and/or the like) maytransmit the second content item for display with the one or more imagesbased on user interaction with the first content item, as describedabove.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, process 600 may include storing, in a datastructure, information identifying the damaged part of the object andthe information regarding the damage on the object in association withinformation identifying the object, receiving another plurality ofimages of the object and the information identifying the object,associating the other plurality of images with the object based on theinformation identifying the object, and identifying, in the otherplurality of images, a location of the damaged part in the otherplurality of images based on the information identifying the damagedpart of the object.

In a second implementation, alone or in combination with the firstimplementation, process 600 may include receiving informationidentifying a plurality of objects and information identifying locationsof damage on the plurality of objects, processing the informationidentifying locations of damage on the plurality of objects to determinea number of defects on each object of the plurality of objects, andtransmitting a listing of the plurality of objects in order based on thenumber of defects on each object.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, the plurality of images: have aplurality of resolutions, have a plurality of aspect ratios, have aplurality of color conditions, have a plurality of lighting conditions,have a plurality of brightness conditions, have a plurality oforientations with respect to the object, or originate from a pluralityof sources.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 600 may include, whenprocessing the plurality of images using the one or more artificialintelligence techniques, processing the plurality of images tostandardize the plurality of images to form a standardized plurality ofimages, wherein the standardized plurality of images has a commonresolution, a common aspect ratio, a common color condition, and/or acommon brightness condition, and processing the standardized pluralityof images to identify the one or more parts of the object that aredepicted in the standardized plurality of images.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, process 600 may include, whenprocessing the plurality of images using the one or more artificialintelligence techniques, assigning the plurality of images to one ormore classes of images based on characteristics of the plurality ofimages and processing one or more of the plurality of images, assignedto one of the one or more classes of images, using an artificialintelligence technique specific to the one of the one or more classes ofimages to identify one or more parts of the object that are depicted inthe one or more of the plurality of images.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,etc.), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: receiving, by a device,information of current inventory of vehicles owned by vehicle dealers,wherein the information of current inventory identifies a plurality ofvehicles and information identifying locations of damage on theplurality of vehicles; processing, by the device, a plurality of imagesof at least a first vehicle and a second vehicle, of the plurality ofvehicles, and information identifying at least the first vehicle and thesecond vehicle using an artificial intelligence technique to identifyone or more parts of at least the first vehicle and the second vehiclethat are depicted in the plurality of images; processing, by the device,information to identify a number of defects of at least the firstvehicle and the second vehicle; processing, by the device, informationidentifying a location of damage on at least the first vehicle and thesecond vehicle to identify a damaged part of at least the first vehicleand the second vehicle; identifying, by the device and from theplurality of images, one or more images depicting the damaged part of atleast the first vehicle and the second vehicle; storing, by the device,information identifying the damaged part of at least the first vehicleand the second vehicle associated with the one or more images and theinformation identifying the at least the first vehicle and the secondvehicle; and transmitting, by the device, a content item for displaywith the one or more images listing at least the first vehicle and thesecond vehicle in order based on the number of defects on each vehicle,wherein the content item includes a close-up image of damage on thedamaged part displayed at a location, in the one or more images, that isadjacent to the location of the damaged part.
 2. The method of claim 1,further comprising: generating the content item for display with the oneor more images, wherein the content item includes information based oninformation regarding the damage on at least the first vehicle and thesecond vehicle.
 3. The method of claim 1, further comprising: receivinganother plurality of images of at least the first vehicle and the secondvehicle; identifying, in the other plurality of images, parts of the atleast the first vehicle and the second vehicle using the artificialintelligence technique; and identifying, in the other plurality ofimages, the damaged part in one or more of the other plurality of imagesand a location of the damaged part based on stored information.
 4. Themethod of claim 1, wherein the information identifying the first vehiclecomprises one or more of: a make of the first vehicle, a model of thefirst vehicle, a year of the first vehicle, a vehicle identificationnumber (VIN), or an image of a license plate.
 5. The method of claim 1,wherein storing the information identifying the damaged part of at leastthe first vehicle and the second vehicle and information identifying atleast the first vehicle and the second vehicle comprises: storing theinformation identifying the damaged part of at least the first vehicleand the second vehicle and information identifying at least the firstvehicle and the second vehicle in a data structure.
 6. The method ofclaim 1, further comprising: providing, to another device,characteristics of the first vehicle including one or more of: vehiclemileage, vehicle price, or vehicle color.
 7. The method of claim 1,further comprising: receiving, from another device, a user selection ofa filtering option; and transmitting, to the other device, the one ormore images depicting the damaged part without transmitting one or moreother images, of the plurality of images, that do not depict the damagedpart.
 8. A device, comprising: one or more memories; and one or moreprocessors, communicatively coupled to the one or more memories,configured to: receive information of current inventory of vehiclesowned by vehicle dealers, wherein the information of current inventoryidentifies a plurality of vehicles and information identifying locationsof damage on the plurality of vehicles; process a plurality of images ofat least a first vehicle and a second vehicle, of the plurality ofvehicles, and information identifying a type of at least the firstvehicle and the second vehicle using an artificial intelligencetechnique to identify one or more parts of at least the first vehicleand the second vehicle that are depicted in the plurality of images;process information to identify a number of defects of at least thefirst vehicle and the second vehicle; process information identifying alocation of damage on at least the first vehicle and the second vehicleto identify a damaged part of at least the first vehicle and the secondvehicle; identify, from the plurality of images, one or more imagesdepicting the damaged part of at least the first vehicle and the secondvehicle; store information identifying the damaged part of at least thefirst vehicle and the second vehicle associated with the one or moreimages and the information identifying the type of the first vehicle andthe type of the second vehicle; and transmit a content item for displaywith the one or more images listing at least the first vehicle andsecond vehicle in order based on the number of defects on each vehicle,wherein the content item includes a close-up image of damage on thedamaged part displayed at a location, in the one or more images, thatdoes not occupy pixels occupied by the damaged part.
 9. The device ofclaim 8, wherein the one or more processors are further configured to:obtain one or more virtual models of the plurality of vehicles toidentify the one or more parts of the first vehicle that are depicted inthe plurality of images using the artificial intelligence technique. 10.The device of claim 8, wherein the one or more processors are furtherconfigured to: generate the content item for display with the one ormore images, wherein the content item includes information based oninformation regarding the damage on the first vehicle.
 11. The device ofclaim 8, wherein the one or more processors are further configured to:receive another plurality of images of the first vehicle; identify, inthe other plurality of images, parts of the first vehicle using theartificial intelligence technique; and identify, in the other pluralityof images, the damaged part in one or more of the other plurality ofimages and a location of the damaged part based on stored information.12. The device of claim 8, wherein the information identifying the firstvehicle comprises one or more of: a make of the first vehicle, a modelof the first vehicle, a year of the first vehicle, a vehicleidentification number (VIN), or an image of a license plate.
 13. Thedevice of claim 8, wherein the one or more processors, when storing theinformation identifying the damaged part of the first vehicle andinformation identifying the first vehicle, are configured to: store theinformation identifying the damaged part of the first vehicle andinformation identifying the first vehicle in a data structure thatincludes vehicle data associated with vehicles owned by a vehicle dealerof the vehicle dealers.
 14. The device of claim 8, wherein the one ormore processors are further configured to: provide to another device,characteristics of the first vehicle including one or more of: vehiclemileage, vehicle price, or vehicle color.
 15. A non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: receive a plurality ofimages of at least a first vehicle and a second vehicle that are part ofan inventory of vehicles owned by vehicle dealers and informationidentifying at least the first vehicle and the second vehicle, whereinthe information identifying the first vehicle includes a vehicleidentification number (VIN); process the plurality of images using anartificial intelligence technique to identify one or more parts of atleast the first vehicle and the second vehicle that are depicted in theplurality of images; process information identifying a location ofdamage on at least the first vehicle and the second vehicle to identifya damaged part of at least the first vehicle and the second vehicle;identify, from the plurality of images, one or more images depicting thedamaged part of at least the first vehicle and the second vehicle; storeinformation identifying the damaged part of at least the first vehicleand the second vehicle associated with the one or more images and theinformation identifying at least the first vehicle and the secondvehicle; and transmit a content item for display with the one or moreimages listing at least the first vehicle and the second vehicle inorder based on the number of defects on each vehicle, wherein thecontent item includes a close-up image of damage on the damaged partdisplayed at a location, in the one or more images, that is adjacent tothe location of the damaged part and that does not occupy pixelsoccupied by the damaged part.
 16. The non-transitory computer-readablemedium of claim 15, wherein the one or more instructions, when executedby the one or more processors, further cause the one or more processorsto: obtain one or more virtual models of the first vehicle to identifythe one or more parts of the first vehicle that are depicted in theplurality of images using the artificial intelligence technique.
 17. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: generate the content item fordisplay with the one or more images, wherein the content item includesinformation based on information regarding the damage on the firstvehicle.
 18. The non-transitory computer-readable medium of claim 15,wherein the one or more instructions, when executed by the one or moreprocessors, further cause the one or more processors to: receive anotherplurality of images of the first vehicle; identify, in the otherplurality of images, parts of the first vehicle using the artificialintelligence technique; and identify, in the other plurality of images,the damaged part in one or more of the other plurality of images and alocation of the damaged part based on stored information.
 19. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: provide to another device,characteristics of the first vehicle including one or more of: vehiclemileage, vehicle price, or vehicle color.
 20. The non-transitorycomputer-readable medium of claim 15, wherein the informationidentifying the first vehicle comprises one or more of: a make of thefirst vehicle, a model of the first vehicle, a year of the firstvehicle, a vehicle identification number (VIN), or an image of a licenseplate.